A novel deep-learning model for automatic detection and classification of breast cancer using the transfer-learning technique
Breast cancer (BC) is one of the primary causes of cancer death among women. Early
detection of BC allows patients to receive appropriate treatment, thus increasing the …
detection of BC allows patients to receive appropriate treatment, thus increasing the …
Unsupervised feature selection via multiple graph fusion and feature weight learning
Unsupervised feature selection attempts to select a small number of discriminative features
from original high-dimensional data and preserve the intrinsic data structure without using …
from original high-dimensional data and preserve the intrinsic data structure without using …
Boosted kernel search: Framework, analysis and case studies on the economic emission dispatch problem
R Dong, H Chen, AA Heidari, H Turabieh… - Knowledge-Based …, 2021 - Elsevier
In recent years, a variety of meta-heuristic nature-inspired algorithms have been proposed to
solve complex optimization problems. However, these algorithms suffer from the …
solve complex optimization problems. However, these algorithms suffer from the …
Boosting slime mould algorithm for parameter identification of photovoltaic models
Y Liu, AA Heidari, X Ye, G Liang, H Chen, C He - Energy, 2021 - Elsevier
Estimating the photovoltaic model's unknown parameters efficiently and accurately can
determine the solar cell's efficacy in converting the solar energy into electricity. For this …
determine the solar cell's efficacy in converting the solar energy into electricity. For this …
Multilevel threshold image segmentation with diffusion association slime mould algorithm and Renyi's entropy for chronic obstructive pulmonary disease
S Zhao, P Wang, AA Heidari, H Chen… - Computers in Biology …, 2021 - Elsevier
Image segmentation is an essential pre-processing step and is an indispensable part of
image analysis. This paper proposes Renyi's entropy multi-threshold image segmentation …
image analysis. This paper proposes Renyi's entropy multi-threshold image segmentation …
Top-k Feature Selection Framework Using Robust 0–1 Integer Programming
Feature selection (FS), which identifies the relevant features in a data set to facilitate
subsequent data analysis, is a fundamental problem in machine learning and has been …
subsequent data analysis, is a fundamental problem in machine learning and has been …
Research on unsupervised feature learning for android malware detection based on restricted Boltzmann machines
Android malware detection has attracted much attention in recent years. Existing methods
mainly research on extracting static or dynamic features from mobile apps and build mobile …
mainly research on extracting static or dynamic features from mobile apps and build mobile …
Hyperspectral band selection via region-aware latent features fusion based clustering
Band selection is one of the most effective methods to reduce the band redundancy of
hyperspectral images (HSIs). Most existing band selection methods tend to regard each …
hyperspectral images (HSIs). Most existing band selection methods tend to regard each …
Student-t kernelized fuzzy rough set model with fuzzy divergence for feature selection
Fuzzy rough set theory can tackle feature redundancy in data and select more informative
features for machine learning tasks. Gaussian kernel is often coupled with fuzzy rough set …
features for machine learning tasks. Gaussian kernel is often coupled with fuzzy rough set …
Bi-level ensemble method for unsupervised feature selection
Unsupervised feature selection is an important machine learning task and thus attracts
increasingly more attention. However, due to the absence of labels, unsupervised feature …
increasingly more attention. However, due to the absence of labels, unsupervised feature …